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Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization

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Abstract

Community detection is one of the most important problems in the field of complex networks in recent years. The majority of present algorithms only find disjoint communities, however, community often overlap to some extent in many real-world networks. In this paper, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) based on spectral-clustering is proposed to detect the overlapping community structure in complex networks. Firstly, the line graph of the graph modeling the network is formed, and a spectral method is employed to extract the spectral information of the line graph. Secondly, IMOQPSO is employed to solve the multi-objective optimization problem so as to resolve the separated community structure in the line graph which corresponding to the overlapping community structure in the graph presenting the network. Finally, a fine-tuning strategy is adopted to improve the accuracy of community detection. The experiments on both synthetic and real-world networks demonstrate our method achieves cover results which fit the real situation in an even better fashion.

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Acknowledgments

This work was supported by the Program for New Century Excellent Talents in University (No. NCET-12-0920), the Program for New Scientific and Technological Star of Shaanxi Province (No. 2014KJXX-45), the National Natural Science Foundation of China (Nos. 61272279, 61272282, 61371201 and 61203303), the Fundamental Research Funds for the Central Universities (Nos. K5051302049, K5051302023, K50511020011, K5051302002 and K5051302028), the Provincial Natural Science Foundation of Shaanxi of China (No. 2011JQ8020), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048) and EU IRSES project (No. 247619).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Yangyang Li, Yang Wang, Jing Chen, Licheng Jiao & Ronghua Shang

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  1. Yangyang Li
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  2. Yang Wang
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  3. Jing Chen
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  4. Licheng Jiao
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  5. Ronghua Shang
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Correspondence to Yang Wang.

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Li, Y., Wang, Y., Chen, J. et al. Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization. J Heuristics 21, 549–575 (2015). https://doi.org/10.1007/s10732-015-9289-y

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  • DOI: https://doi.org/10.1007/s10732-015-9289-y

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